Introduction

Over the past few decades, data has gained increasing importance and relevance in seemingly all social, economic and political contexts. From social relations and communications to retail operations and public administration and governance, digital technologies and automated systems have been embedded in the workings of many societies; the amount of data collected about people as they move through those societies has grown at a startling pace.

As a result, research about data, its value, management, uses and impacts – what we call ‘data-related research’ – is being conducted in a wide range of areas, sometimes in unexpected places and ways. As well as inside academia in fields with an obvious interest in data, like computer science, data studies and the philosophy of science, research is also taking place in less-obvious fields like psychology, art, agriculture and literature. Research is also being conducted outside academia in the private, public and third sectors, in the media, in consultancies, in community organisations and across industry.

And yet, despite the widespread importance of data in so many diverse parts of life – or perhaps because of its widespread importance – it is extremely difficult to have a comprehensive view of all the available research, writing and communication about data. It seems that since data is everywhere, research about data can be potentially everywhere as well. The fragmented and dispersed nature of this emerging research field creates a number of challenges. For researchers, it can be difficult to identify relevant research and findings in adjacent areas of enquiry. This can make it difficult to build on previous work, collaborate on shared interests, or coordinate to avoid duplicated effort. For the potential users of this research, it can be difficult to locate the findings, insights and evidence that are relevant to their circumstances and needs. For funders and supporters of data-related research, it can be difficult to spot peaks and troughs of funding, identify under-examined areas or coordinate with others to produce the greatest impact.

To help provide visibility of this field of research, trace developments, and link funding, research and impact, we conducted a nine-month-long mixed-methods research project. We drew on methodologies such as landscape reviews, ‘research on research’ , scientometric analysis and bibliometric analysis , all of which have demonstrated their value in other fields of investigation and research such as funding for climate change and cancer research . These methodologies can even be used to examine the link between funding, research and impact in the past – which can help to understand the present and think strategically about the future.

We believe this research will be useful for a range of audiences.

• First, it will help funders to be more targeted in their funding and to identify emerging topics.
• Second, it will help researchers to easily identify the overall trends of the field and collaborate with other researchers who are interested in sharing effort and expertise.
• Finally, for those who use research results, this work will hopefully help them have a clear understanding of the findings and evidence being gathered across the data-related research landscape that best suits their work.

The rest of the report is made up of the following sections:

• The Methodology section defines our research questions and describes the mixed-methods approach we used to understand the data-related landscape.
• The Findings section presents the results from our qualitative and quantitative research
  • In the Qualitative section we review interviewees’ answers about the emerging field of data-related research, how fragmented it is, and how these funders cooperate and plan for areas of future funding.
  • In the Quantitative section we analyse data we gathered on how dispersed this field is, who is funding it, how much they are funding, who is receiving funding, and where they are publishing their work.
• The Discussion and future work section reviews our findings and our plans to continue working in this area.
  • We discuss gaps in the evidence base, particularly non-academic grey literature, the need for more open research data sources, and better standardised funding information.
  • Lastly, we outline questions for future research , including speaking to many more people, finding more sources of data, and building discovery tools to help others better understand this field.

This report has been researched and produced by the Open Data Institute, and published in March 2022. Its authors are Jared Robert Keller, Sara Marcucci, Joe Massey, Fionntán O’Donnell and Annalisa Eichholzer. To share feedback by email or to get in touch, contact the Evidence & Foresight programme at, research.team@theodi.org